The term, Big Data, was coined by Michael Cox and David Ellsworth in a 1997 article for a conference on visualization. The term was more about the size of the data sets that taxed the memory of computer systems. When data didn’t fit in memory, the solution was to get more resources. It is the first article in the ACM digital library to use the term “big data.”Three Vs of Big data were first mentioned by Doug Laney in 2001 Volume – amount of data is massiveVelocity – speed at which data are being generated is very fastVariety– different types of data like structured and unstructuredVeracity – data must be accurate and truthfulVolume – humans create 2.5 quintillion bytes of data daily, 90% of today’s data has been generated in the past two years. created and machine createdVelocity – a study by 360i found that brands receive 350,000 Facebook likes per minute Velocity – 600,000 tweets per hour

The term,Big Data, is more of an amalgamation of different areas that help us try to get a handle on, insight from and use out of data. The focus needs to be on the data and less about the big. Data has always been big. I wrote my first book on a Mac Plus. Each chapter required its own floppy disk.

When they linked their customer feedback to operational metrics, they got more value from the data as measured by executive satisfaction with the program and higher customer loyalty rankings within their industry.

Linkage analysis helps answer important questions that help senior management better manage its business. 1. What is the $ value of improving customer satisfaction/loyalty?2. Which operational metrics have the biggest impact on customer satisfaction/loyalty?3. Which employee/partner factors have the biggest impact on customer satisfaction/loyalty?The bottom line is that linkage analysis helps the company understand the causes and consequences of customer satisfaction and loyalty and thereby helping senior manager better manage its business.Understand the causes and consequences of customer satisfaction/loyalty

Here is how a company can set up their data to identify customer-centric self- service metrics. For each service transaction, we have a corresponding satisfaction rating of that experience and the objective metrics behind that experience. We can then run analyses to show how the objective metrics are related to customer satisfaction with that experience.

Slide 11: Identifying Customer-Centric MetricsHere is the result of some hypothetical data that shows how objective metrics impact customer satisfaction with the experience. We see that customers who spent more time on the site were less satisfied with the experience than customers who spent less time on the site. Further, returning customers to the support site reported higher satisfaction with the experience compared to new customers to the support site.Manage customer relationships using objective operational metrics: By understanding and identifying which objective metrics are predictive of customer satisfaction with self service, you can use the objective metrics to help design the service experience to improve customer satisfaction. Predict customer satisfaction without surveys: Transactional survey response rates are typically low, around 10%. So, how do we know about the other 90% of customers who do not complete a survey? Based on our linkage analysis of these 10%, we can apply the predictive model to the other 90% to estimate customer satisfaction based solely on the objective metrics. Using web analytics of online behavior patterns, companies might be able to profile customers who are predicted to be dissatisfied and intervene during the transaction to either improve their service experience or ameliorate its negative impact.The bottom line is that you don’t need to rely solely on customer satisfaction ratings; if you haven’t yet, consider including objective metrics in your service measurement strategy.

Avoid putting people on projects who are vested in the old way of doing things.

Kate Crawford from MITBe concerned about common method variance – things are correlated only because they are measured by the same thing

Kate Crawford, a Microsoft researcher and MIT professor, said that 2013 will be the year in which we reach the peak of the Big Data hype.According to Gartner’s hype cycle of emerging technologies,Big Data is headed toward it’s peak of inflated expectations and won’t reach the plateau of productivity for 2 to 5 years. The Plateau of Productivity represents the time when the technology finally delivers predictable value. The promise of Big Data, of course, is a treasure trove of high value across many industries – including healthcare. Everything from predictive and prescriptive analytics to population health, disease management, drug discovery and personalized medicine (delivered with much greater precision and higher efficacy) to name but a few.

Accenture surveyed 600 executives from the US and UK about their use of analytics. They found that the adoption of analytics is up and continues to grow, ROI remains elusive. Strategies usually are about making decisions. And when we make a decision, we typically eliminate an alternative course of action. Tactics are usually much more flexible. Strategies are about “what” we choose to do. Tactics are about “how” we choose to do it. It is often easier to change the “how” we do things than the “what”.Strategies are the investments of resources that build and grow an organization. Tactics are the day to day actions that get us to our goals.

Customer Loyalty Measurement ApproachesObjectiveTime spent on web siteNumber products/servicespurchasedRenewed contractSubjective (self-reported)Likelihood to recommendLikelihood to continue purchasingLikelihood to renew contractHere is a figure that illustrates how different loyalty metrics fit into the larger customer loyalty measurementframework of loyalty types and measurement approaches. It is important to point out that the subjective measurement approach is not synonymous with emotional loyalty. Survey questions can be used to measure both emotional loyalty (e.g., overall satisfaction) as well as behavioral loyalty (e.g., likelihood to leave, likelihood to buy different products). In my prior research on measuring customer loyalty, I found that you can reliably and validly measure the different types of loyalty using survey questions.– I conducted several studies a few years ago, examining different types of customer loyalty questions. As the business models suggested, I found that loyalty questions generally fall into three types of loyalty behaviors. The table here includes these three types (Retention, Advocacy and Purchasing) and some survey questions for each type of loyalty.likelihood to switch providers (retention)likelihood to renew service contract (retention)likelihood to recommend (advocacy)overall satisfaction (advocacy)likelihood to purchase different solutions from &lt;Company Name&gt; (purchasing)likelihood to expand use of &lt;Company Name’s&gt; products throughout company (purchasing)These questions can be used as individual measures. If you are using multiple questions, you can calculate an average of the questions within a given type of loyalty. So, if you were using all six of these questions, you could calculate three scores, one for retention, one for advocacy and one for purchasing loyalty, each an average score of their corresponding questions.

3.
Big Data Definition
An amalgamation of different
areas* that help us get a
handle on, insight from
and use out of data
* includes technology (Data Capture, Storage & Management, BI Reporting) and analytics

4.
Big Interest in Big Data: Google Trends
Customer Experience
Big Data
Scale is based on the average worldwide traffic of Customer Experience and Big Data from January
2004 to January 2014.

47.
Importance of CX is Over-inflated
• Correlations between loyalty ratings and
CX satisfaction ratings are driven by the
fact that we use the same measurement
method to measure each (ratings on bipolar scale in web survey)
• Correlation between customer experience
and recommending behavior:
– CX and Likelihood to recommend: r = .52
– CX and Number of friends/colleagues: r = .28
• Consider using objective loyalty metrics
www.businessoverbroadway.com/is-the-importance-of-customer-experience-over-inflated

48.
Value from Analytics: Accenture 2012 Study
1. Focus on Strategic Issues - only 39%
said that the data they generate is
"relevant to the business strategy"
2. Measure Right Customer Metrics - only
20% were very satisfied with the business
outcomes of their existing analytics
programs
3. Integrate Business Metrics - Half of the
executives indicated that data integration
remains a key challenge to them.
http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx